Prediction and retrospective analysis of soccer matches in a league

Haavard Rue*, Øyvind Salvesen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

Abstract

A common discussion subject for the male part of the population in particular is the prediction of the next week-end's soccer matches, especially for the local team. Knowledge of offensive and defensive skills is valuable in the decision process before making a bet at a bookmaker. We take an applied statistician's approach to the problem, suggesting a Bayesian dynamic generalized linear model to estimate the time-dependent skills of all teams in a league, and to predict the next week-end's soccer matches. The problem is more intricate than it may appear at first glance, as we need to estimate the skills of all teams simultaneously as they are dependent. It is now possible to deal with such inference problems by using the Markov chain Monte Carlo iterative simulation technique. We show various applications of the proposed model based on the English Premier League and division 1 in 1997-1998: prediction with application to betting, retrospective analysis of the final ranking, the detection of surprising matches and how each team's properties vary during the season.

Original languageEnglish (US)
Pages (from-to)399-418
Number of pages20
JournalJournal of the Royal Statistical Society Series D: The Statistician
Volume49
Issue number3
DOIs
StatePublished - Jan 1 2000

Keywords

  • Dynamic models
  • Generalized linear models
  • Graphical models
  • Markov chain Monte Carlo methods
  • Prediction of soccer matches

ASJC Scopus subject areas

  • Statistics and Probability

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